Intention recognition is the process of inferring an agent's intention by analyzing their actions and their actions' effects on the environment
[1]}. Approaches in action recognition, goal
recognition and plan recognition have been used to infer intention. According to [2]}, intention recognition
systems can be classified as logic-based, classical machine learning,
deep learning and brain-inspired approaches, or they can be classified
in terms of the behavior of the observed human towards the observer. We take here a simplified view, and consider two classes of intention
recognition approaches: logic-based and probabilistic.
Logic-based approaches are defined by a set of domain-independent rules
that capture the relevant knowledge to infer the human's intention
through deduction [3]}. The knowledge can be
represented in different ways, including using plan representation languages
like STRIPS and PDDL that describe the state of the environment and the
effects of the possible actions.
Logic-based approaches work well in highly structured environments.
Logic representation can define different kinds of relationships
depending on the problem. These relationships allow to recognize humans'
intentions based on observations. Another advantage of logic-based
approaches is that they are highly expressive. The reasoning result can
potentially be traceable and human-understandable. However, many
logic-based approaches assume that the human is rational and try to find
the optimal intention that best fits the observations, while humans
often act in non-rational ways [4]}. This makes
logic-based approaches less reliable in real-world problems. The
uncertainty in humans' rationality might be addressed by a combination
of logic-based with probabilistic reasoning techniques.
Probabilistic approaches exploit Bayesian networks and Markov models.
Bayesian networks are generative probabilistic graphical models that
represent random variables as nodes and conditional dependencies as
arrows between them [2]}. They can
provide the probability distribution of any set of random variables
given another set of observed variables. Some planning systems use Bayesian inference to reason about
intention. Such approaches are referred to as Bayesian inverse
planning. [6]} propose an approximate planning method
that generates a set of possible goals by using Bayesian inverse
planning methods on classical planning. The method assumes that humans
are perfectly rational, which means they only optimally pursue their
goals. As a result of this, the indecisive behavior of humans is not
tolerated. This limitation is partly addressed in [7]}, that
introduces a more general formulation.
[8]} offers an example of using Bayesian
inference in a logic based approach. The authors use classical planning
to generate an action plan for each goal, then they use a Bayesian prior
function to infer human intention. Probabilistic approaches are able to handle uncertainty, and can
therefore handle real-world settings such as non-rational agents,
interrupted plans and partial observability
[2]}. On the other hand, they are less
expressive than logic-based systems, since it is hard to understand the
reasoning behind the result. Scalability is another, well-known
difficulty with probabilistic approaches.
